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Traffic Accident Management System for Intelligent and Sustainable Vehicle Networking 20

Traffic Accident Management System for Intelligent and Susta...

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20th ACS/ieee international conference on Computer systems and applications (AICCSA)

作者： Radi, Wafaa El Badawy, Hesham M. Mudassir, Ahmed Kamel, Hesham CIC Canadian Int Coll Dept Commun & Elect Engn Cairo Egypt Natl Telecommun Inst Network Planning Dept Cairo Egypt Comsats Univ Islamabad Dept Elect & Comp Engn Lahore Pakistan Canadian Int Coll Dept Elect & Commun Giza Egypt

ISBN: (纸本)9798350319439

Intelligent and sustainable vehicle networking (ISVN) is a new paradigm for transportation that makes use of developments in vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication to encourage collaboration between vehicles and the infrastructure in order to enhance traffic flow, safety, and environmental effect. Traffic accidents are a major public safety concern, resulting in significant casualties and economic losses each year, as well as a significant contributor to air pollution and greenhouse gas emissions. In order to increase traffic accident management's efficacy and efficiency and support a more environmentally friendly transportation system, this article suggests an integrated ISVN-ML traffic accident management system. The proposed system leverages ISVN sensors and cameras to collect data about traffic accidents in real time. This data is then transmitted to an ML-based traffic accident management system, which uses it to predict the number of killed and injured people involved in the accident and to immediately prioritize the dispatch of emergency responders. Additionally, the system provides real-time information to police stations and ambulance services to help them respond to accidents more quickly and efficiently. After the police station has completed its investigation of the accident, the details of the accident are sent back to the ML-based traffic accident management system to improve the accuracy of the system's predictions and make the system more efficient over time. Overall, the proposed system is a promising approach to improving traffic safety, reducing the economic costs associated with traffic accidents, and contributing to a more sustainable transportation system.

关键词： Traffic Accident Management Machine Learning Internet Of Vehicles (IoV) Intelligent Transportation systems (ITS) real-time Predictions Emergency Responders

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Terabit Optical Feeder Links for DVB Satellite systems: real-time End-to-End Communication System Design & Field Test Results

Terabit Optical Feeder Links for DVB Satellite Systems: Real...

引用

international conference on Space Optical systems and applications (ICSOS)

作者： Korevaar, C. W. Bui, T. -C. Boschma, J. J. Toet, P. Silvestri, F. Broekens, K. A. do Amaral, G. C. Vos, Y. Veldhuis, E. P. Tatman, B. Klop, W. A. Ferrario, I. Netherlands Org Appl Res TNO Delft Netherlands Celestia STS Noordwijk Netherlands

ISBN: (纸本)9798350307900;9798350307917

This paper presents the design and field test results of a real-time communication system for the transport of DVB signals over optical feeder links (OFLs). The design relies on 1. multiplexing and massive parallel processing to get to Tbps datarates, 2. error correction in combination with DDR-based interleaving to mitigate the effects of turbulence-induced fading, 3. careful selection of coding rate and interleave depths to minimize latency and 4. ensuring that the OFL-induced degradations on the end-to-end performance are negligible such that the usual suspects - the RF user downlink and the RF amplifier - remain the limiting factors. To verify the system performance, a ground-to-ground test has been conducted over 9.8 km. During the field tests we experienced strong turbulence-induced fading causing about 20 dB of fading penalty (at 1% outage probability). However, as we show in the paper, by properly choosing the coding, coding rate and interleaving depth we can achieve 10 dB in coding and diversity gain (at a BER of 1E-6). With the proposed real-time system, we demonstrate the transmission of DVB signals, in particular 16APSK 9/10 using a 28 Gbps/wavelength OFL with wavelength division multiplexing (WDM). With the presented results, we aim to pave the way to actual OFLs fueling the next generation of very high throughput satellites and constellations.

关键词： real time systems

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Automated Convict Identification Through Machine Learning-Based Facial Recognition

Automated Convict Identification Through Machine Learning-Ba...

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2024 ieee international conference on computing, applications and systems, COMPAS 2024

作者： Zubary, Nadim N. Khondoker, Shanzida Ferdows, Rubayea Ahmed, Fuad IUBAT-International University of Business Agriculture and Technology Dept of Computer Science and Engineering Bangladesh Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798331529765

In a world riddled with criminal incidents, the swift identification of wrongdoers remains an ongoing challenge. Criminals have become adept at erasing biological and fingerprint traces from crime scenes, rendering traditional investigative methods ineffective. Modern facial identification technologies offer a rapid solution to this problem. With the widespread use of CCTV cameras, driven by security technology advancements, a unique opportunity arises. Our research seeks to automatically detect and identify convicts from video footage, providing a powerful tool for law enforcement. This technology extends to identifying suspects, criminals, missing persons, and more, saving valuable time and effort. Our study focuses on developing a machine learning-based criminal face detection system to streamline law enforcement by automating criminal identification. Employing a mixed methods approach involving data collection and focus group discussions, we gain insights from field practitioners. Our survey reveals a significant challenge in identifying culprits, with nearly 60% of cases analyzed yielding unidentified perpetrators. We propose a novel machine learning based automated method, which integrates with a comprehensive database containing individuals' information and diverse images. Once identified, the system conducts real-time database comparisons, providing immediate results when a match is found. We explore techniques for criminal identification from photographs and introduce innovative ideas. Our research comprehensively covers various models, offering a promising solution to the challenges of locating and identifying convicts in modern law enforcement. © 2024 ieee.

关键词： Crime

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KubeEdge for Scalable IoT applications 7

KubeEdge for Scalable IoT Applications

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7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Saini, Lalit Mohan Ajmani, Prerna Asha, V. Pithamber, Khobragade Sreevani, N. Viveknanda Institute of Professional Studies Tc Ggsipu Delhi India New Horizon College of Engineering Department of Master of Computer Application Bangalore India Mlr Institute of Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Dundigal Hyderabad500043 India

ISBN: (纸本)9798350350067

The rise of IoT (Internet of Things) presents both opportunities and challenges in managing large-scale deployments. Traditional cloud solutions struggle with realtime data processing and scalability for numerous connected devices. KubeEdge, an extension of Kubernetes, addresses these challenges by extending cloud-native orchestration to the edge of the network. This paper explores how KubeEdge enhances IoT applications through efficient edge computing, crucial for reducing latency and optimizing resource utilization. KubeEdge extends Kubernetes' capabilities to manage containerized applications on edge nodes, enabling localized data processing and reducing reliance on centralized cloud infrastructures. This approach minimizes latency and lowers bandwidth costs, making it ideal for real-time, data-intensive IoT applications. This research paper highlights practical use cases and proposes strategic deployment frameworks, emphasizing KubeEdge's benefits in scalability, resource management, and operational efficiency. It also addresses potential challenges in deploying KubeEdge at scale, such as managing resource distribution and integrating with existing infrastructure. This paper provides actionable insights and best practices for Utilizing KubeEdge in various IoT contexts, demonstrating its impact on improving the performance and scalability of IoT systems. © 2024 ieee.

关键词： Resource allocation

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A Scalable Clustered Architecture for Cyber-Physical systems 21

A Scalable Clustered Architecture for Cyber-Physical Systems

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ieee 21st international conference on Industrial Informatics (INDIN)

作者： Cabral, Bernado Costa, Pedro Fonseca, Tiago Ferreira, Luis Lino Pinho, Luis Miguel Ribeiro, Pedro INESC TEC ISEP Porto Portugal EVOLEO Technol Maia Portugal

ISBN: (纸本)9781665493130

Developing distributed and scalable Cyber-Physical systems (CPS) that can handle large amounts of data at high data rates at the edge, remains a challenging task. Also, the limited availability of open-source solutions makes it difficult for developers and researchers to experiment with and deploy CPSs on a larger scale. This work introduces Edge4CPS, an open-source multi-architecture solution built over Kubernetes that aims to enable an easy to use, efficient and scalable solution for the deployment of applications on edge-like distributed computing clusters. To verify the successful real-world implementation of the introduced architecture, the system was tested in a railway scenario, derived from the Ferrovia 4.0 project, which highlights its functionalities.

关键词： Edge computing Cloud Open-Source Kubernetes Cluster Network

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Application Methodology for Search-Based applications to Improve the Safety, Quality, and Reliabiliy of ECS-Based Products 12

Application Methodology for Search-Based Applications to Imp...

引用

12th ieee international conference on Intelligent systems, IS 2024

作者： Kahlert, Toralf Pumacy Technologies Ag Uberwatch GmbH Brandenburg Berlin Germany

ISBN: (纸本)9798350350982

The constantly increasing use of electronic components and systems with ever-higher intelligence and performance entails the requirement to analyze the data they constantly generate in almost real-time, to put them into a suitable context with each other and with surrounding data to ultimately make intelligent decisions to improve safety, quality, and reliability of products. The following article is intended to show how such tasks can be completed using search-based applications. Research approaches from industrial applications are shown as case studies in which machine, sensor, and field data are processed using this technology. © 2024 ieee.

关键词： Artificial Intelligence (AI) Data Analytics Deep Learning Electronic Components and systems (ECS) Machine Learning (ML) Process Mining Reliability Sensor Fusion

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A Web-based Visualization System for Operating System Resource Management 48

A Web-based Visualization System for Operating System Resour...

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48th Annual ieee international Computers, Software, and applications conference (COMPSAC) - Digital Development for a Better Future

作者： Su, Ting-Yu Chiang, Mei-Ling Chen, Sih-Bei Hsu, Yun-Han Yang, Hsiao-Tung Yeh, Run-An Natl Chi Nan Univ Dept Informat Management Nantou Taiwan

ISBN: (纸本)9798350376975;9798350376968

This research developed a web-based visualization system demonstrating operating system resource management algorithms. The core of this system is the design and implementation of an online operating system simulator, which offers users a visual way to learn and understand the resource management algorithms used in operating systems. These algorithms include CPU scheduling, real-time scheduling, deadlock, page replacement, and disk scheduling. Users are encouraged to actively engage with the system by inputting different datasets and related parameters. The system then generates workloads based on these inputs and executes the designated algorithms automatically, allowing users to store their workloads in the system database. Moreover, the system facilitates a step-by-step execution of algorithms, visually presenting the changing status of system resources and detailed execution results, thereby fostering a sense of active involvement in the learning process. The system is designed with a user-friendly interface that is easy to operate and track the operation results. The purpose is to help users understand the execution process and algorithms easily, thereby significantly enhancing their learning effectiveness. Once the evaluation of learning effectiveness is completed, the proposed system will be available online to benefit more people interested in learning operating systems more effectively.

关键词： operating systems visualization software algorithms resource management computer-assisted instruction

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Blockchain-Based Consent Management for Privacy Persevering and Transparency in Intelligent Surveillance systems 5

Blockchain-Based Consent Management for Privacy Persevering ...

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5th international conference on Blockchain computing and applications, BCCA 2023

作者： Jaafar, Fehmi Amayed, Darine Salhab, Wissam Bouani, Hajer Cheriet, Mohamed Quebec University at Chicoutimi Department of Computer Science Canada École de Technologie Supérieure Systems Engineering Department Canada ENSI L'École Nationale des Sciences de l'Informatique Tunisia

ISBN: (纸本)9798350339239

Video cameras are becoming ubiquitous. They'd be interconnected within a larger pervasive infrastructure supported by artificial intelligence which helps ensure smart services relaying in real-time video analysis and image processing. However, such services and technologies involve a set of privacy concerns. If privacy concerns can be addressed, enabling real-time analytics on video streams in public spaces can be valuable in supporting many applications for security, public safety, and urban space management. This study aims to highlight privacy concerns in image and video processing in the context of intelligent IoT systems by proposing a blockchain-based approach for data transparency and privacy management. As a result, our proposed approach will provide IoT customers the ability to manage their consent using a distributed ledger securely. © 2023 ieee.

关键词： Blockchain

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TGIEN: An Interpretable Image Editing Method for IoT applications Based on Text Guidance 17

TGIEN: An Interpretable Image Editing Method for IoT Applica...

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ieee Congress on Cybermatics: 17th ieee international conference on Internet of Things, iThings 2024, 20th ieee international conference on Green computing and Communications, GreenCom 2024, 17th ieee international conference on Cyber, Physical and Social computing, CPSCom 2024, 10th ieee international conference on Smart Data, SmartData 2024

作者： Wang, Yifei Pan, Mengzhu Li, Qianmu Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798350351637

In response to the demands and challenges of image editing in the Internet of Things (IoT) domain, we have proposed an interpretable image editing method guided by text(TGIEN), aiming to address the limitations of existing image editing models based on Generative Adversarial Networks (GANs) for better application in IoT. This method utilizes a Contrastive Language-Image Pre-trained Model (CLIP) to guide the editing of the StyleGAN latent space, enabling manipulation of real images through text prompts. The model incorporates a mapping module conditioned on text embeddings to edit the latent space code of StyleGAN and learns an attention map corresponding to the text across all layers of StyleGAN. These attention maps highlight focus areas during editing, providing an intuitive explanation of the model's behavior. Additionally, during the image generation process, the attention map functions as a mask to guide local fusion between original and modified features. Furthermore, the model integrates editability losses to ensure that editing operations align closely with the text prompts. Through the aforementioned design, spatial disentanglement is explicitly achieved, enhancing the transparency and interpretability of the model and in turn increasing the capacity of image editing technology in the IoT domain for practical application and system optimization. Both qualitative and quantitative experiments demonstrate the superiority of this method over other state-of-the-art methods in terms of editing quality and decoupling of edits. In the IoT domain, this method holds promise for applications such as smart surveillance systems, smart healthcare and virtual reality, offering more intelligent and efficient image processing and analysis for these scenarios. © 2024 ieee.

关键词： Generative adversarial networks

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Towards real-world Deployment of NILM systems: Challenges and Practices 14

Towards Real-world Deployment of NILM Systems: Challenges an...

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14th ieee international conference on Sustainable computing and Communications, SustainCom 2024

作者： Xue, Junyu Zhang, Yu Wang, Xudong Wang, Yi Tang, Guoming The Chinese Univ of Hong Kong Hong Kong Hong Kong The Chinese Univ of Hong Kong Shenzhen China Southern University of Science and Technology Shenzhen China Pengcheng Laboratory Shenzhen China Heyuan Bay Area Digital Economy Technology Innovation Center Heyuan China Guangzhou China

ISBN: (纸本)9798331521059

Non-intrusive load monitoring (NILM), as a key load monitoring technology, can much reduce the deployment cost of traditional power sensors. Previous research has largely focused on developing cloud-exclusive NILM algorithms, which often result in high computation costs and significant service delays. To address these issues, we propose a three-tier framework to enhance the real-world applicability of NILM systems through edgecloud collaboration. Considering the computational resources available at both the edge and cloud, we implement a lightweight NILM model at the edge and a deep learning based model at the cloud, respectively. In addition to the differential model implementations, we also design a NILM-specific deployment scheme that integrates Gunicorn and NGINX to bridge the gap between theoretical algorithms and practical applications. To verify the effectiveness of the proposed framework, we apply real-world NILM scenario settings and implement the entire process of data acquisition, model training, and system deployment. The results demonstrate that our framework can achieve high decomposition accuracy while significantly reducing the cloud workload and communication overhead under practical considerations. © 2024 ieee.

关键词： Edge-cloud collaboration Energy management Machine learning Non-intrusive load monitoring

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